2025-04-10
About healthiar
healthiar function examples
Post-healthiar workflow
Q & A
healthiarhealthiar 1/5healthiar is an R package (= collection of R functions)
The healthiar functions allow you to quantify and monetize the health impacts of environmental stressors (air pollution & noise)
healthiar 2/5Figure: healthiar overview. CBA = Cost-benefit analysis; DALY = disability-adjusted life years; GBD = global burden of disease; MDI = multidimensional deprivation index; PAF = population attributable fraction; PIF = population impact fraction; YLD = years lived with disability; YLL = years of life lost
healthiar 3/5Let’s jump right in, with an example of a healthiar R function call
healthiar 4/5Selection of healthiar core family members (functions)
attribute_health() to env. exposure with either relative or absolute riskcompare() two scenariossummarize_uncertainty() Monte Carlo simulationmonetize() health impactshealthiar 5/5Installation & getting started with healthiar: see the README file
README file of the healthiar R package on GitHub
healthiar in RStudio 1/2Landing page of the healthiar package in RStudio, where you find the package vignettes and function documentation.
healthiar in RStudio 2/2Any arguments without a = symbol after the name have no default and must be user-specified
attribute_health() with RRattribute_health() with RRGoal: attribute COPD cases to air pollution
Tip
healthiar comes with some example data that start with exdat_ that allow you to test functions.
Every attribute output initially consists of two main lists (“folders”), and additional sub-lists (“sub-folders”)
health_main contains the main results
health_detailed contains more detailed results and additional information
impact_raw contains detailed results
input_table contains the input data as entered in the function call
input_args = function arguments (list) as used by R in the background
The output tables are in the tibble format, which is a modern version of the original data frame, and can be used like a data frame
Tip
Different ways exist and you might have a personal preference!
Go to the Environment tab in RStudio …
… and click on a variable to “open” it.
Alternatively, you can use View(results_noise_ha), which has the same effect.
results_pm_copd$health_main$impact_rounded
Note: after typing the $ sign you can see all available options by pressing the tab key and use the arrows & tab keys to select an option (or alternatively use the mouse)
| impact_rounded | impact | pop_fraction | erf_ci | rr | exp | bhd |
|---|---|---|---|---|---|---|
| 3502 | 3501.962 | 0.1138961 | central | 1.369 | 8.85 | 30747 |
Some relevant columns include:
rr_central, ..._lower or ..._upper was used to obtain impactattribute_health() with RR & uncertaintyattribute_health() with RR & uncertaintyGoal: attribute COPD cases to PM2.5 exposure
In health_detailed each row contains results for a different/unique input argument uncertainty combination:
rr_central with exp_central and bhd_central
rr_lower with exp_central and bhd_central
…
| exp_ci | bhd_ci | erf_ci | pop_fraction | impact | geo_id_disaggregated | is_lifetable | prop_pop_exp | rr_increment | erf_shape | approach_risk | exposure_dimension | exposure_type | exp | rr | bhd | cutoff_ci | cutoff | duration_ci | duration | pop_fraction_type | rr_conc | impact_rounded |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| central | central | central | 0.1138961 | 3501.9619 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.369 | 30747 | central | 5 | central | 1 | paf | 1.128536 | 3502 |
| central | lower | central | 0.1138961 | 3189.0894 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.369 | 28000 | central | 5 | central | 1 | paf | 1.128536 | 3189 |
| central | upper | central | 0.1138961 | 3644.6736 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.369 | 32000 | central | 5 | central | 1 | paf | 1.128536 | 3645 |
| central | central | lower | 0.0440064 | 1353.0658 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.124 | 30747 | central | 5 | central | 1 | paf | 1.046032 | 1353 |
| central | lower | lower | 0.0440064 | 1232.1801 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.124 | 28000 | central | 5 | central | 1 | paf | 1.046032 | 1232 |
| central | upper | lower | 0.0440064 | 1408.2058 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.124 | 32000 | central | 5 | central | 1 | paf | 1.046032 | 1408 |
| central | central | upper | 0.1780300 | 5473.8882 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.664 | 30747 | central | 5 | central | 1 | paf | 1.216589 | 5474 |
| central | lower | upper | 0.1780300 | 4984.8398 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.664 | 28000 | central | 5 | central | 1 | paf | 1.216589 | 4985 |
| central | upper | upper | 0.1780300 | 5696.9598 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.85 | 1.664 | 32000 | central | 5 | central | 1 | paf | 1.216589 | 5697 |
| lower | central | central | 0.0899213 | 2764.8092 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.369 | 30747 | central | 5 | central | 1 | paf | 1.098806 | 2765 |
| lower | lower | central | 0.0899213 | 2517.7955 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.369 | 28000 | central | 5 | central | 1 | paf | 1.098806 | 2518 |
| lower | upper | central | 0.0899213 | 2877.4806 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.369 | 32000 | central | 5 | central | 1 | paf | 1.098806 | 2877 |
| lower | central | lower | 0.0344604 | 1059.5528 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.124 | 30747 | central | 5 | central | 1 | paf | 1.035690 | 1060 |
| lower | lower | lower | 0.0344604 | 964.8902 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.124 | 28000 | central | 5 | central | 1 | paf | 1.035690 | 965 |
| lower | upper | lower | 0.0344604 | 1102.7316 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.124 | 32000 | central | 5 | central | 1 | paf | 1.035690 | 1103 |
| lower | central | upper | 0.1416706 | 4355.9450 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.664 | 30747 | central | 5 | central | 1 | paf | 1.165054 | 4356 |
| lower | lower | upper | 0.1416706 | 3966.7760 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.664 | 28000 | central | 5 | central | 1 | paf | 1.165054 | 3967 |
| lower | upper | upper | 0.1416706 | 4533.4583 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 8.00 | 1.664 | 32000 | central | 5 | central | 1 | paf | 1.165054 | 4533 |
| upper | central | central | 0.1453304 | 4468.4726 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.369 | 30747 | central | 5 | central | 1 | paf | 1.170043 | 4468 |
| upper | lower | central | 0.1453304 | 4069.2501 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.369 | 28000 | central | 5 | central | 1 | paf | 1.170043 | 4069 |
| upper | upper | central | 0.1453304 | 4650.5716 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.369 | 32000 | central | 5 | central | 1 | paf | 1.170043 | 4651 |
| upper | central | lower | 0.0567717 | 1745.5580 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.124 | 30747 | central | 5 | central | 1 | paf | 1.060189 | 1746 |
| upper | lower | lower | 0.0567717 | 1589.6063 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.124 | 28000 | central | 5 | central | 1 | paf | 1.060189 | 1590 |
| upper | upper | lower | 0.0567717 | 1816.6929 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.124 | 32000 | central | 5 | central | 1 | paf | 1.060189 | 1817 |
| upper | central | upper | 0.2247829 | 6911.4001 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.664 | 30747 | central | 5 | central | 1 | paf | 1.289961 | 6911 |
| upper | lower | upper | 0.2247829 | 6293.9214 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.664 | 28000 | central | 5 | central | 1 | paf | 1.289961 | 6294 |
| upper | upper | upper | 0.2247829 | 7193.0531 | 1 | FALSE | 1 | 10 | log_linear | relative_risk | 1 | population_weighted_mean | 10.00 | 1.664 | 32000 | central | 5 | central | 1 | paf | 1.289961 | 7193 |
summarize_uncertainty() for overall confidence intervalssummarize_uncertainty() for overall confidence intervalsYou can do a Monte Carlo simulation via the summarize_uncertainty function.
The outcome added to inputted results variable (results_pm_copd in this case)
Two folders are added:
uncertainty_main contains the central estimate (median of simulated estimates) and the corresponding 95% confidence intervals obtained through the Monte Carlo assessment
uncertainty_detailed contains all n_sim simulations
Tip
This is work in progress!
Distributions used in the simulations (to be added to function documentation):
Relative risk (per increment) gamma distribution
Exposure, baseline health data & cutoff normal distribution
Disability weight beta distribution
Relative risk user-defined ERF to be added
Absolute risk user-defined ERF normal distribution
| central_estimate | lower_estimate | upper_estimate |
|---|---|---|
| 3361.85 | 1179.711 | 5689.718 |
| rr | exp | bhd | rr_conc | paf | impact_total |
|---|---|---|---|---|---|
| 1.285688 | 9.508392 | 31403.72 | 1.11996 | 0.1071111 | 3363.688 |
| 1.531341 | 8.329302 | 30356.39 | 1.152434 | 0.1322713 | 4015.278 |
| 1.138642 | 9.209543 | 28722.15 | 1.056176 | 0.0531883 | 1527.683 |
| 1.379912 | 9.527935 | 31147.49 | 1.156975 | 0.1356768 | 4225.991 |
| 1.609170 | 9.725841 | 30868.47 | 1.252094 | 0.2013377 | 6214.985 |
| 1.426400 | 8.411436 | 29991.41 | 1.128804 | 0.1141064 | 3422.211 |
| 1.192879 | 8.332883 | 31521.85 | 1.060544 | 0.0570876 | 1799.506 |
| 1.133884 | 8.737630 | 29288.22 | 1.048083 | 0.0458772 | 1343.662 |
| 1.536306 | 8.931032 | 29886.18 | 1.183873 | 0.1553145 | 4641.758 |
| 1.412146 | 8.406493 | 31063.27 | 1.124751 | 0.1109143 | 3445.360 |
| 1.417912 | 8.477226 | 29683.65 | 1.129099 | 0.1143377 | 3393.960 |
| 1.377327 | 9.417160 | 30554.96 | 1.1519 | 0.1318693 | 4029.261 |
| 1.286310 | 8.765111 | 29136.53 | 1.099436 | 0.0904425 | 2635.181 |
| 1.546691 | 9.343722 | 31961.40 | 1.20857 | 0.1725756 | 5515.758 |
| 1.478402 | 8.595755 | 32563.51 | 1.150941 | 0.1311460 | 4270.576 |
| 1.354317 | 9.322901 | 31823.58 | 1.140096 | 0.1228809 | 3910.509 |
| 1.297481 | 8.891400 | 32220.22 | 1.106655 | 0.0963758 | 3105.249 |
| 1.332347 | 8.791603 | 30143.28 | 1.114936 | 0.1030874 | 3107.393 |
| 1.223978 | 7.898470 | 32190.05 | 1.06033 | 0.0568971 | 1831.521 |
| 1.350225 | 8.124512 | 31202.96 | 1.098362 | 0.0895534 | 2794.332 |
| 1.139003 | 9.212674 | 29513.46 | 1.05636 | 0.0533534 | 1574.643 |
| 1.205776 | 8.189381 | 30416.80 | 1.061498 | 0.0579349 | 1762.193 |
| 1.209341 | 8.306106 | 32429.23 | 1.064857 | 0.0609071 | 1975.172 |
| 1.318152 | 8.933384 | 31125.40 | 1.114774 | 0.1029576 | 3204.595 |
| 1.321549 | 9.317071 | 30829.88 | 1.127905 | 0.1134004 | 3496.121 |
| 1.344183 | 8.700120 | 31836.60 | 1.115658 | 0.1036680 | 3300.437 |
| 1.477832 | 9.273465 | 29406.00 | 1.18165 | 0.1537254 | 4520.449 |
| 1.458768 | 8.207489 | 31741.91 | 1.128752 | 0.1140656 | 3620.659 |
| 1.439575 | 9.361821 | 29868.83 | 1.172247 | 0.1469372 | 4388.841 |
| 1.353460 | 9.239273 | 28925.77 | 1.136903 | 0.1204172 | 3483.161 |
| 1.401861 | 8.577939 | 30981.19 | 1.12847 | 0.1138447 | 3527.043 |
| 1.198644 | 9.006337 | 32037.70 | 1.075291 | 0.0700190 | 2243.248 |
| 1.333433 | 9.464992 | 30018.33 | 1.137102 | 0.1205715 | 3619.354 |
| 1.339450 | 8.338405 | 29214.03 | 1.102486 | 0.0929593 | 2715.717 |
| 1.533942 | 8.394773 | 31386.63 | 1.15632 | 0.1351873 | 4243.074 |
| 1.211297 | 8.865382 | 29850.50 | 1.07691 | 0.0714177 | 2131.854 |
| 1.418315 | 8.634695 | 30187.88 | 1.135441 | 0.1192852 | 3600.967 |
| 1.242320 | 8.723763 | 31369.69 | 1.084152 | 0.0776203 | 2434.924 |
| 1.350507 | 9.178648 | 31440.92 | 1.133783 | 0.1179973 | 3709.943 |
| 1.302968 | 8.961486 | 30422.95 | 1.110531 | 0.0995302 | 3028.004 |
| 1.356083 | 9.628013 | 32025.18 | 1.151389 | 0.1314841 | 4210.801 |
| 1.400387 | 8.327527 | 30339.46 | 1.118573 | 0.1060040 | 3216.103 |
| 1.220972 | 9.115051 | 31576.35 | 1.085625 | 0.0788717 | 2490.481 |
| 1.156492 | 8.862623 | 30829.04 | 1.057766 | 0.0546114 | 1683.616 |
| 1.218216 | 8.354771 | 31989.60 | 1.068461 | 0.0640740 | 2049.701 |
| 1.391985 | 7.984684 | 31719.73 | 1.103749 | 0.0939970 | 2981.560 |
| 1.414935 | 9.188174 | 30893.99 | 1.156461 | 0.1352931 | 4179.745 |
| 1.293503 | 8.807688 | 30457.87 | 1.102954 | 0.0933442 | 2843.067 |
| 1.675114 | 8.191074 | 31813.84 | 1.178947 | 0.1517853 | 4828.874 |
| 1.183939 | 8.999369 | 30533.11 | 1.06986 | 0.0652986 | 1993.770 |
| 1.404241 | 8.800046 | 31356.47 | 1.137702 | 0.1210352 | 3795.238 |
| 1.424605 | 9.665176 | 29133.15 | 1.179509 | 0.1521893 | 4433.755 |
| 1.369328 | 8.377727 | 31146.49 | 1.112009 | 0.1007271 | 3137.295 |
| 1.492353 | 9.278921 | 29832.61 | 1.186857 | 0.1574383 | 4696.795 |
| 1.659857 | 8.522931 | 31572.71 | 1.195445 | 0.1634911 | 5161.856 |
| 1.295024 | 8.105655 | 32480.59 | 1.083601 | 0.0771514 | 2505.924 |
| 1.240427 | 9.450927 | 29169.46 | 1.100646 | 0.0914430 | 2667.344 |
| 1.265808 | 7.687133 | 31790.43 | 1.065388 | 0.0613744 | 1951.119 |
| 1.448872 | 8.988882 | 30491.86 | 1.159399 | 0.1374841 | 4192.147 |
| 1.361141 | 9.131160 | 29423.94 | 1.135841 | 0.1195951 | 3518.960 |
| 1.198617 | 9.056507 | 30884.86 | 1.076259 | 0.0708556 | 2188.366 |
| 1.256037 | 8.541922 | 31381.55 | 1.084091 | 0.0775685 | 2434.219 |
| 1.362793 | 8.379576 | 29713.20 | 1.110278 | 0.0993245 | 2951.249 |
| 1.415730 | 9.094852 | 30580.49 | 1.152986 | 0.1326871 | 4057.636 |
| 1.311285 | 8.904021 | 31733.36 | 1.111602 | 0.1003972 | 3185.942 |
| 1.257928 | 8.268175 | 31101.58 | 1.077877 | 0.0722506 | 2247.106 |
| 1.408206 | 8.354344 | 30916.77 | 1.121677 | 0.1084777 | 3353.780 |
| 1.517698 | 8.304259 | 29473.72 | 1.147806 | 0.1287723 | 3795.400 |
| 1.422765 | 7.879501 | 29742.09 | 1.106865 | 0.0965476 | 2871.526 |
| 1.317354 | 9.387687 | 31197.23 | 1.128552 | 0.1139090 | 3553.645 |
| 1.390649 | 8.778709 | 30112.01 | 1.132707 | 0.1171595 | 3527.909 |
| 1.295307 | 8.655817 | 28398.93 | 1.099212 | 0.0902574 | 2563.213 |
| 1.395169 | 9.603162 | 31293.30 | 1.165666 | 0.1421212 | 4447.441 |
| 1.276655 | 9.161596 | 32740.78 | 1.10699 | 0.0966492 | 3164.371 |
| 1.546330 | 9.644315 | 32004.02 | 1.224385 | 0.1832632 | 5865.157 |
| 1.298726 | 10.012797 | 30335.59 | 1.139998 | 0.1228055 | 3725.376 |
| 1.581896 | 8.609282 | 29488.03 | 1.180019 | 0.1525559 | 4498.573 |
| 1.329638 | 8.958514 | 30421.52 | 1.119387 | 0.1066536 | 3244.564 |
| 1.387484 | 10.210339 | 31184.47 | 1.186057 | 0.1568702 | 4891.915 |
| 1.618744 | 9.310252 | 30655.81 | 1.230725 | 0.1874708 | 5747.070 |
| 1.367207 | 9.965514 | 30882.52 | 1.168016 | 0.1438471 | 4442.361 |
| 1.529293 | 9.153735 | 31624.09 | 1.192978 | 0.1617619 | 5115.572 |
| 1.285948 | 9.019776 | 29893.38 | 1.106383 | 0.0961537 | 2874.358 |
| 1.355785 | 9.563891 | 31039.88 | 1.149028 | 0.1296990 | 4025.842 |
| 1.425144 | 9.392363 | 31274.19 | 1.16837 | 0.1441067 | 4506.820 |
| 1.435735 | 8.743207 | 32429.35 | 1.144975 | 0.1266188 | 4106.166 |
| 1.330381 | 8.849186 | 27760.64 | 1.116145 | 0.1040591 | 2888.747 |
| 1.283693 | 8.644385 | 30337.79 | 1.095286 | 0.0869963 | 2639.274 |
| 1.429891 | 9.525869 | 27957.70 | 1.175677 | 0.1494264 | 4177.618 |
| 1.325542 | 8.579015 | 30282.23 | 1.106127 | 0.0959444 | 2905.411 |
| 1.434679 | 8.935600 | 31701.95 | 1.152637 | 0.1324239 | 4198.094 |
| 1.403908 | 8.210236 | 30399.22 | 1.115062 | 0.1031893 | 3136.873 |
| 1.376632 | 9.551326 | 30439.68 | 1.156593 | 0.1353915 | 4121.274 |
| 1.274944 | 9.517273 | 31507.04 | 1.115972 | 0.1039201 | 3274.216 |
| 1.498075 | 8.357447 | 30029.44 | 1.14534 | 0.1268969 | 3810.643 |
| 1.482485 | 8.246144 | 31988.57 | 1.136334 | 0.1199768 | 3837.888 |
| 1.420453 | 9.239489 | 32093.30 | 1.160436 | 0.1382549 | 4437.056 |
| 1.409355 | 8.755419 | 30888.66 | 1.137531 | 0.1209033 | 3734.542 |
| 1.445617 | 8.809513 | 31192.22 | 1.150728 | 0.1309846 | 4085.700 |
| 1.632728 | 9.208270 | 32800.93 | 1.229136 | 0.1864203 | 6114.760 |
attribute_health() with RR & user-defined ERFattribute_health() with RR & user-defined ERFA user-defined exposure-response function can be fed to the erf_eq_... arguments
Any function of the form intercept + a x c^1 + b x c^2 + …
Any other (non-linear) function of the function type, as obtained from e.g. splinefun() or approxfun()
| impact_rounded | pop_fraction | erf_ci | exp | bhd |
|---|---|---|---|---|
| 2033 | 0.0661067 | central | 8.85 | 30747 |
attribute_health() with ARattribute_health() with ARGoal: attribute cases of high annoyance (HA) to noise exposure
| exposure_dimension | exp | pop_exp | impact |
|---|---|---|---|
| 1 | 57.5 | 387500 | 49674.594 |
| 2 | 62.5 | 286000 | 50788.595 |
| 3 | 67.5 | 191800 | 46813.105 |
| 4 | 72.5 | 72200 | 23657.232 |
| 5 | 77.5 | 7700 | 3298.314 |
attribute_health()attribute_health()Goal: attribute disease cases to PM2.5 exposure in multiple geographic units, such as municipalities, provinces, countries, …
results_iteration <- attribute_health(
geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"),
geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
rr_central = 1.369,
rr_increment = 10,
cutoff_central = 5,
erf_shape = "log_linear",
exp_central = list(11, 11, 10, 8, 7),
bhd_central = list(4000, 2500, 3000, 1500, 500)
)Here the we want to aggregate results by language region ("Ger", "Fra", "Ita")
results_iteration <- attribute_health(
geo_id_disaggregated = c("Zurich", "Basel", "Geneva", "Ticino", "Valais"),
geo_id_aggregated = c("Ger","Ger","Fra","Ita","Fra"),
rr_central = 1.369,
rr_increment = 10,
cutoff_central = 5,
erf_shape = "log_linear",
exp_central = as.list(c(11, 11, 10, 8, 7)),
bhd_central = as.list(c(4000, 2500, 3000, 1500, 500))
)Use as.list() if you input vectors.
Tip
For iterations, enter geo unit-specific inputs as a list
Feed unique geo ID’s as a vector to the geo_id_disaggregated argument (e.g. municipality names)
Optional: aggregate geo unit-specific results by providing higher-level ID’s (e.g. region names) to the geo_id_aggregated argument (as a vector)
Tip
The main output contains aggregated results if available, or disaggregated results if no aggregation ID was provided
| geo_id_aggregated | impact_rounded | erf_ci | exp_ci | bhd_ci |
|---|---|---|---|---|
| Fra | 466 | central | central | central |
| Ger | 1116 | central | central | central |
| Ita | 135 | central | central | central |
| geo_id_disaggregated | geo_id_aggregated | impact_rounded |
|---|---|---|
| Zurich | Ger | 687 |
| Basel | Ger | 429 |
| Geneva | Fra | 436 |
| Ticino | Ita | 135 |
| Valais | Fra | 30 |
compare() two scenarioscompare() two scenariosattribute_health() to calculate burden of scenarios A & Bcompare() to compare scenarios A & BThe compare() results are very similar to attribute_health() results:
health_main contains main comparison results
health_detailed
impact_raw raw comparison results
scenario_1 contains results of scenario 1 (scenario A in our case)
scenario_2 contains results of scenario 2 (scenario B in our case)
| impact | impact_rounded | impact_1 | impact_2 | bhd | exp_1 | exp_2 | rr_conc_1 | rr_conc_2 |
|---|---|---|---|---|---|---|---|---|
| 773.5564 | 774 | 1050.86 | 277.304 | 25000 | 8.85 | 6 | 1.043879 | 1.011217 |
monetize() health impactsmonetize() health impactsDifferent monetization pathways / options are available
monetize() outputmonetize() adds two main lists (“folders”) to the inputted health impacts
monetization_main contains total results
monetization_detailed contains detailed results
by_year yearly results| impact | monetized_impact | discount_rate | valuation | monetized_impact_before_inflation_and_discount | monetized_impact_after_inflation_and_discount |
|---|---|---|---|---|---|
| 3501.962 | 60416.46 | 0.03 | 20 | 70039.24 | 60416.46 |
Additional existing healthiar functions
attribute_lifetable() for YLLattribute_health with the arguments dw & duration for YLDget_daly as the sum of YLL and YLDprepare_exposure() combines spatial exposure data and spatial geographic units data to obtain (pop-weighted) mean exposureattribute_health() with exposure categories and RRsocialize determine burden attributable to differences in exposure that are caused by differences in a social indicator (e.g. share of health impacts attributable to the fact that a person lives in a poor neighborhood with high exposure instead of a rich neighborhood with low exposure)attribute_mod() modify an existing healthiar assessmentcba() cost-benefit analysishealthiar workflowVisualization is out of scope of healthiar. You can visualize in
ggplot2 package (online book by the creator)